Graph neural networks (GNNs)[1]} first extended neural network architectures to graphs. As a result, many related works were sprung up ensuingly. Spectral approaches, established on spectral graph theory, are among the most critical parts of these works. Bruna et al.[2]} first defined convolution operations on Fourier domain. However, the filters this model computed are non-spatially localized, so Henaff et al.[3]} improved it by generating spatially localized filters. Kipf et al.[4]} proposed graph convolutional networks (GCNs), which further simplified above methods by using a first-order approximation on the Chebyshev polynomials. Unlike GCNs, Veličković et al.[5]} proposed graph attention networks (GATs) to dynamically aggregate node features. Numerous variants have been derived from such design. Wang et al.[6]} introduced heterogeneous graph attention networks (HANs) to process various heterogeneous graphs. Ma et al.[7]} put forward disentangled graph convolutional networks using a routing algorithm. Several other works also made efforts to learn graph representations. GraphSAGE[8]} generates node embeddings by aggregating node features using several pre-defined aggregate operations. Inspired by RNNs like LSTM[9]} and GRU[10]}, gated graph neural networks[11]}, namely GGNNs, were proposed. Furthermore, Xu et al.[12]} explored jumping knowledge networks, in which layer aggregation is adopted to acquire multi-scale features.
